PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Two grammatical inference applications in music processing
P. P. Cruz and Enrique Vidal
Applied Artificial Intelligence Volume 22, Number 1 & 2, pp. 53-76, 2008. ISSN 1087-6545 (electronic) 0883-9514 (paper)

Abstract

This article reviews our work in the field of music processing (MP) using grammatical inference (GI), where regular grammars are used for modeling musical style. These models can be used to generate automatic composition (AC) and classify music by style (musical style identification) with their resulting applications. The latter, for instance, would improve content-based retrieval in multimedia databases, joining indexing by musical style to other suitable indexes. In this work, several GI techniques are used to learn from examples of melodies, stochastic grammars for different musical styles. Then, each of the learned grammars is used to generate new melodies (composition) or to classify test melodies (style identification). Our studies in this field show the need of proper music coding schemes, so different coding schemes are presented and compared. Results from our previous studies have been improved, achieving in style identification a classification error rate that ranges from 0.5 to 1.7%, depending on the corpus used.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4553
Deposited By:Alfons Juan
Deposited On:24 March 2009